A Bi-Objective Stochastic Closed-loop Supply Chain Network Design Problem Considering Downside Risk

Amir Mohammad Fathollahi Fard,Fatemeh Gholian-Jouybari,Mohammad Mahdi Paydar,Mostafa Hajiaghaei-Keshteli 대한산업공학회 2017 Industrial Engineeering & Management Systems Vol.16 No.3

This paper deals with a closed-loop supply chain network and proposes new approaches in metaheuristics and exact methods as solution methods. Moreover, the downside risk is incorporated into the objective functions as a risk measure. Hence, the developed two-stage stochastic model aims to minimize the expected total cost and the downside risk, simultaneously. Besides, this study presents the closed-loop network which considers the forward and reverse networks in an integrated manner. In the forward network, this case just considers the forward network, while, the reverse logistic fully focused on the backward network by considering recovering centers (i.e. from recovering centers to remanufacture, recycling and disposal centers). In order to address the problem, ICA, PSO, GA, and also ɛ-constraint method, are utilized. In addition, the parameters of algorithms are tuned by Response Surface Method (RSM) with an MODM approach. To explain the efficiency and effectiveness of methods, four assessment metrics are introduced. At the end, the results show the capability of ICA through the most of the tests problem. According to the risk management, a real data set is used to do some sensitivity analyses on the proposed model.

A Bi-Objective Stochastic Closed-loop Supply Chain Network Design Problem Considering Downside Risk

Fard, Amir Mohammad Fathollahi,Gholian-Jouybari, Fatemeh,Paydar, Mohammad Mahdi,Hajiaghaei-Keshteli, Mostafa Korean Institute of Industrial Engineers 2017 Industrial Engineeering & Management Systems Vol.16 No.3

This paper deals with a closed-loop supply chain network and proposes new approaches in metaheuristics and exact methods as solution methods. Moreover, the downside risk is incorporated into the objective functions as a risk measure. Hence, the developed two-stage stochastic model aims to minimize the expected total cost and the downside risk, simultaneously. Besides, this study presents the closed-loop network which considers the forward and reverse networks in an integrated manner. In the forward network, this case just considers the forward network, while, the reverse logistic fully focused on the backward network by considering recovering centers (i.e. from recovering centers to remanufacture, recycling and disposal centers). In order to address the problem, ICA, PSO, GA, and also ${\varepsilon}$-constraint method, are utilized. In addition, the parameters of algorithms are tuned by Response Surface Method (RSM) with an MODM approach. To explain the efficiency and effectiveness of methods, four assessment metrics are introduced. At the end, the results show the capability of ICA through the most of the tests problem. According to the risk management, a real data set is used to do some sensitivity analyses on the proposed model.

Bio-recovery of municipal plastic waste management based on an integrated decision-making framework

Mohammad M. Shahsavar,Mehran Akrami,Zahra Kian,Mohammad Gheibi,Amir M. Fathollahi-Fard,Mostafa Hajiaghaei-Keshteli,Kourosh Behzadian 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.108 No.-

Recent years have seen a rapid development in industrialization and urbanization with a huge growth inthe population throughout the world. In this regard, an efficient and robust decision-making frameworkfor the concept of a green city and sustainable development goals to manage municipal plastic wastes isstill needed. This study models a bio-recovery of municipal different plastic wastes management basedon a new integrated Multi-Criterion Decision-Making (MCDM) approach through a case study inMashahd, Iran. The proposed integrated MCDM framework includes the Shannon Entropy (SE),Ordered Weighted Aggregation (OWA), Analytic Hierarchy Process (AHP), Technique for OrderPreference by Similarity to Ideal Solution (TOPSIS) and, ELimination Et Choice Translating REality(ELECTRE) systems in an intelligent way. Through decision-making computations, all criteria areapproved after extraction from the literature review by experts with more than 60% agreement percentage. Different scenarios of economic, energy, and environmental crises are created. One finding of thispaper is to propose a new entrance in economic competition with plastic biodegradation to present anovel, environmental-friendly product with high-quality and low-cost advantages. Another findingdetermines that with an application of plastic wastes bio-recovery, citizens’ satisfaction from urban managementsystem will be increased from 49% to 64%. Whereas, based on the outcomes of this investigation,the rate of municipal waste industries development, smart city goals’ meeting, and rate of hazardousmaterial emission from municipal solid wastes are increased to 58%, 25%, and 70%, respectively. Thedeclared numerical outcomes illustrate the effectiveness of plastic waste bio-recovery on the smart cityconcept.

A biobjective home health care logistics considering the working time and route balancing: a self-adaptive social engineering optimizer

Fariba Goodarzian,Ajith Abraham,Amir Mohammad Fathollahi-Fard 한국CDE학회 2021 Journal of computational design and engineering Vol.8 No.1

Home health care (HHC) logistics have become a hot research topic in recent years due to the importance of HHC services for the care of ageing population. The logistics of HHC services as a routing and scheduling problem can be defined as the HHC problem (HHCP) academically including a set of service centers and a large number of patients distributed in a specific geographic environment to provide various HHC services. The main challenge is to provide a valid plan for the caregivers, who include nurses, therapists, and doctors, with regard to different difficulties, such as the time windows of availability for patients, scheduling of the caregivers, working time balancing, the time and cost of the services, routing of the caregivers, and route balancing for their routes. This study establishes a biobjective optimization model that minimizes (i) the total service time and (ii) the total costs of HHC services to meet the aforementioned limitations for the first time. To the best of the authors’ knowledge, this research is the first of its kind to optimize the time and cost of HHC services by considering the route balancing. Since the model of the developed HHCP is complex and classified as NP-hard, efficient metaheuristic algorithms are applied to solve the problem. Another innovation is the development of a new self-adaptive metaheuristic as an improvement to the social engineering optimizer (SEO), so-called ISEO. An extensive analysis is done to show the high performance of ISEO in comparison with itself and two well-known metaheuristics, i.e. FireFly algorithm and Artificial Bee Colony algorithm. Finally, the results confirm the applicability of new suppositions of the model and further development and investigation of the ISEO more broadly.

A novel particle swarm optimization-based grey model for the prediction of warehouse performance

Md. Rakibul Islam,Syed Mithun Ali,Amir Mohammad Fathollahi-Fard,Golam Kabir 한국CDE학회 2021 Journal of computational design and engineering Vol.8 No.2

Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse’s key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)—the GM (1, 1) model—is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model—PSOGM (1, 1)—to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model—GAGM (1, 1)—the discrete grey model—DGM (1, 1)—to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.